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- W4320147287 abstract "AbstractAlmost all methods of parameter estimation in statistical models begin with the likelihood function. This chapter discusses the theory of maximum likelihood in regular finite-dimensional models, where the parameter space is a finite-dimensional smooth manifold. The Bartlett identities for log likelihood derivatives are derived, and the Bartlett adjustment for likelihood-ration statistics is discussed. These ideas are illustrated by a range of examples from linear models and generalized linear models, including the estimation of the LD50 or other dose quantile in a logistic model setting.A sketch of the argument is given for generalized linear models, Gaussian variance-component models and mixture models, including sparse-signal detection problems." @default.
- W4320147287 created "2023-02-13" @default.
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- W4320147287 date "2022-01-01" @default.
- W4320147287 modified "2023-09-28" @default.
- W4320147287 title "Likelihood" @default.
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- W4320147287 doi "https://doi.org/10.1007/978-3-031-14275-8_17" @default.
- W4320147287 hasPublicationYear "2022" @default.
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